Developmental brain disorders are among the most interesting and challenging research areas in modern neuroscience. Dyslexia is an extremely complicated example of such disorders that severely impairs the learning abilities of children. It is characterized by substandard reading skills which remain unaccounted for after considering an individual's age, overall intelligence, and education. Similarly, autism is another example of a brain disorder that is characterized by qualitative abnormalities in behavior and higher cognitive functions. It typically appears during the first three years of life and can severely impact the development of social interaction and communication skills.
Conventionally, identifying individuals having such brain disorders required observation by a trained professional to identify one or more characteristics typical of the disorder. For example, with respect to autism conventional autism diagnostics rely on recording patient reactions to varied stimuli through periodic screening interviews. Early observations by parents can greatly reduce the false positive rate and circumvent unnecessary referrals. However, the diagnosis is subject to human observational and perceptional errors because autism has many forms and personality traits that may be difficult to detect. A more objective computer aided diagnosis (CAD) is a prime necessity in this field.
Advances in neuro-imaging provide some possibilities for non-invasive methods for automatic dyslexia and/or autism detection by revealing differences between quantitative characteristics of normal, autistic and dyslexic brains. Studies have shown a relation between the cerebral white matter (CWM) volume and anatomy in dyslexic and autistic brains as compared to normal brains (see, e.g., M. Casanova et al., “Reduced brain size and gyrification in the brains of dyslexic patients,” Journal of Child Neurology, 2004, vol. 9, issue 4, pp. 275-281 M. Klingberg et al., “Microstructure of Temporo-Parietal White Matter as a Basis for Reading Ability: Evidence from Diffusion Tensor Magnetic Resonance Imaging,” Neuron, 2000, vol. 25, pp. 493-500; S. Niogi and B. McCandliss, “Left lateralized white matter microstructure accounts for individual differences in reading ability and disability,” Neuropsychologia, 2006, vol. 44, pp. 2178-2188; E. Aylward et al., “Effects of age on brain volume and head circumference in autism,” Neurology, 2002, vol. 59, issue 2, pp. 175-183; and E. Courchesne et al., “Evidence of brain overgrowth in the first year of life in autism,” Journal of American Medical Association, 2003, vol. 290, pp. 337-344).
Particularly, the CWM structural differences may generally be related to the volume of the CWM, where an autistic brain is reported as having a larger volume than a normal brain and a dyslexic brain is reported as having a smaller volume than a normal brain. Some conventional computer aided diagnoses systems utilize a volumetric analysis to classify a brain as autistic or dyslexic. However, a volumetric approach fails to account for other factors including, for example, age and gender. Thus, while volume has been linked to autism and dyslexia, computer aided analysis utilizing volume as a discriminating factor is not particularly accurate because of brain volume differences due to age and gender.
Therefore, a need continues to exist in the art for an improved image processing techniques for use in analyzing medical images of the brain.